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A novel learning-to-rank based hybrid method for book recommendation

Published: 23 August 2017 Publication History

Abstract

Recommendation system is able to recommend items that are likely to be preferred by the user. Hybrid recommender systems combine the advantages of the collaborative filtering and content-based filtering for improved recommendation. Hybrid recommendation methods use as many significant factors as possible to generate recommendation, which is practically very functional in real scenarios. However, such method has not been applied to book recommendation yet. Thus, in this paper, we propose a set of novel features which can be categorized into three types: latent features, derived features and content features. These features can be combined to form a new hybrid feature vector containing rating information and content information. Then, we adopted learning-to-rank to use the proposed feature vector as the input for book recommendation. Collaborative Ranking (CR) and Probabilistic Matrix Factorization (PMF) are compared with our proposed method. The experimental results show that the proposed method outperforms CR and PMF. It shows that, on NDCG@1, PMF achieves 0.713818, CR achieves 0.690072 vs. our method achieves 0.742689 which is 4.04% over PMF and 7.62% over CR.

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  1. A novel learning-to-rank based hybrid method for book recommendation

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    cover image ACM Conferences
    WI '17: Proceedings of the International Conference on Web Intelligence
    August 2017
    1284 pages
    ISBN:9781450349512
    DOI:10.1145/3106426
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    Publication History

    Published: 23 August 2017

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    Author Tags

    1. collaborative filtering
    2. hybrid recommendation
    3. ranking-based recommendation
    4. recommendation system

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    • Natural Science Foundation of China

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    WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
    Overall Acceptance Rate 118 of 178 submissions, 66%

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    Cited By

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    • (2024)A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildingsPLOS ONE10.1371/journal.pone.030321419:5(e0303214)Online publication date: 16-May-2024
    • (2024)Book recommendation system: reviewing different techniques and approachesInternational Journal on Digital Libraries10.1007/s00799-024-00403-725:4(803-824)Online publication date: 1-Dec-2024
    • (2021)What books will be your bestseller? A machine learning approach with Amazon KindleThe Electronic Library10.1108/EL-08-2020-023439:1(137-151)Online publication date: 5-Apr-2021
    • (2020)Research on Recommendation Method of Product Design Scheme Based on Multi-Way Tree and Learning-to-RankMachines10.3390/machines80200308:2(30)Online publication date: 5-Jun-2020
    • (2020)Optimal Recommendation Strategy Identification towards Energy Efficiency in Tertiary Buildings2020 5th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech49282.2020.9243701(1-6)Online publication date: 23-Sep-2020
    • (2020)Persuasion-based recommender system ensambling matrix factorisation and active learning modelsPersonal and Ubiquitous Computing10.1007/s00779-020-01382-728:1(247-257)Online publication date: 12-Mar-2020
    • (2019)Persuade Me!: A User-Based Recommendation System Approach2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00310(1740-1745)Online publication date: Aug-2019

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